Three-dimensional risk maps

ABSTRACT

Systems including one or more sensors, coupled to a vehicle, may detect sensor information and provide the sensor information to another computing device for processing. The processing may include analyzing the sensor information to identify one or more risk objects, such as animals, pedestrians, potholes, etc. The processing may further include generating a three-dimensional (3D) risk map, which may be displayed to a passenger in the vehicle. The 3D risk map may illustrate the one or more risk objects as one or more point clouds, respectively, within a virtual world representation of the vehicle&#39;s surroundings. Such a display may be used to alert drivers to possible risks while driving. In case of an accident, the 3D risk map may also be leveraged by insurance providers to process insurance claims and to notify customers that an insurance claim has been established.

BACKGROUND

Recently, many vehicles come equipped with global positioning system(GPS) devices that help drivers to navigate roads to various locations.Moreover, many drivers use other mobile devices (e.g., smart phones)that have GPS devices therein to help the drivers navigate roads. TheseGPS devices may provide location information and use maps for navigationpurposes. As GPS devices have become more prevalent, the different usesfor their location information have come to light. In some instances,the danger level of different routes is determined by combining locationinformation and accident history information. Although some entities mayfind the danger level of certain routes useful and interesting, suchinformation alone might not significantly reduce the likelihood ofaccidents occurring. Therefore, there remains a desire for methods andsystems that may help drivers avoid accidents. Moreover, in the event ofan accident, there is a desire for methods and systems that utilizeinformation regarding the environment in which the accident occurred tohelp other drivers avoid a similar accident.

Additionally, it is difficult to use the location information todetermine the cause of the accident when the location information merelyincludes GPS coordinates. Insurance providers may find determining thecause of an accident particularly important. When an accident occurs, adriver (or insurance policy holder of the damaged vehicle) may file aninsurance claim with an insurance provider to cover the cost ofrepairing a vehicle. The insurance provider may wish to determine thecause of the accident in order to properly process the insurance claimand curtail insurance fraud. In some cases, employees of an insuranceprovider (e.g., insurance adjusters) may be dispatched to inspect adamaged vehicle or to inspect the site of the accident to determine thecause of the accident. Inspecting the site or vehicle may be importantfor record keeping or determining whether insurance coverage isapplicable. As such, insurance customers may become frustrated andinconvenienced by the processing time of an insurance claim.Accordingly, there is a desire for methods and systems that facilitatethe automatic processing of insurance claims.

SUMMARY

The following summary is for illustrative purposes only and is notintended to limit or constrain the detailed description. The followingsummary merely presents various described aspects in a simplified formas a prelude to a more detailed description provided below.

Various approaches to helping users identify and mitigate risk arepresented. In accordance with aspects of the disclosure, a computingsystem may generate, based on a vehicle traveling on a segment of road,a three-dimensional (3D) map for identifying and alerting a user of apotential risk (e.g., a risk object). The system may receive varioustypes of information, including but not limited to, accidentinformation, geographic information, environmental information, riskinformation, and vehicle information from one or more sensors. Thesystem may generate a 3D risk map using the received information. Thesystem may calculate a risk value (e.g. risk score, route risk score,road risk score, road segment risk score, risk object risk score, etc.)and associate the risk score to a particular road segment. Further, thesystem may provide alerts to a user by indicating an identification of arisk object based on the calculated risk score of the risk object.

In other aspects of the present disclosure, a personal navigationdevice, mobile device, and/or personal computing device may communicate,directly or indirectly, with a server (or other device) to transmit andreceive a risk score(s), a 3D risk map(s), and/or received information.The device may receive travel route information and query the memory forassociated risk scores and 3D risk maps. The risk scores may be sent fordisplay on the device (via the 3D risk map) or for recording in memory.The contents of memory may also be uploaded to a system data storagedevice for use by a network device (e.g., server) to perform variousactions. For example, an insurance company may use the informationstored in the system data storage device to take various actions (e.g.,file a claim, adjust a user's insurance premium, etc.).

In other aspects of the disclosure, a personal navigation device, mobiledevice, and/or personal computing device may access a database of riskscores to assist in identifying and indicating alternate lower-risktravel routes. A driver may select among the various travel routespresented, taking into account one or more factors such as the driver'stolerance for risk or the driver's desire to lower the cost of theirinsurance. These factors may be saved in memory designating the driver'spreferences. Depending on the driver's selection or preference, the costor other aspects of the vehicle's insurance coverage may be adjustedaccordingly for either the current insurance policy period or a futureinsurance policy period.

Certain other aspects of the disclosure include a system comprising oneor more sensors coupled to a vehicle and configured to detect sensorinformation. The system may also include a first computing deviceconfigured to communicate with the one or more sensors to receive thesensor information; analyze the sensor information to identify one ormore risk objects; generate a three-dimensional (3D) risk mapillustrating the one or more risk objects as one or more point clouds,respectively, within a virtual world representation of the vehicle'ssurroundings; and display the 3D risk map to a passenger in the vehicle.

Certain other aspects of the disclosure may include a system comprisingone or more sensors coupled to a vehicle and configured to detect sensorinformation. The system may also include a first computing deviceconfigured to communicate with the one or more sensors to receive thesensor information; analyze the sensor information to identify one ormore risk objects; generate a three-dimensional (3D) risk mapillustrating the one or more risk objects as one or more point clouds,respectively, within a virtual world representation of the vehicle'ssurroundings; store the 3D risk map; determine, based on the sensorinformation, that the vehicle was in an accident; analyze the 3D riskmap to determine a cause of the accident; and construct, based on thecause of the accident, an insurance claim.

The details of these and other aspects of the disclosure are set forthin the accompanying drawings and descriptions below. Other features andadvantages of aspects of the disclosure may be apparent from thedescriptions and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features, aspects, and advantages of the presentdisclosure will become better understood with regard to the followingdescription, claims, and drawings. The present disclosure is illustratedby way of example, and not limited by, the accompanying figures in whichlike numerals indicate similar elements.

FIG. 1 illustrates an example operating environment in accordance withaspects of the present disclosure.

FIG. 2 depicts an example of sensors coupled to a vehicle in accordancewith aspects of the present disclosure.

FIG. 3 depicts another example operating environment in accordance withaspects of the present disclosure.

FIG. 4 depicts an illustrative user interface that may be displayed inaccordance with aspects of the present disclosure.

FIG. 5 depicts illustrative images that may be displayed in accordancewith aspects of the present disclosure.

FIG. 6 depicts a flowchart of an example process in accordance withaspects of the present disclosure.

FIG. 7 depicts a flowchart of an example process in accordance withaspects of the present disclosure.

FIG. 8A depicts a flowchart of an example process in accordance withaspects of the present disclosure.

FIG. 8B depicts a flowchart of an example process in accordance withaspects of the present disclosure.

FIG. 8C depicts a flowchart of an example process in accordance withaspects of the present disclosure.

DETAILED DESCRIPTION

In accordance with various aspects of the disclosure, methods,non-transitory computer-readable media, and apparatuses are disclosedfor generating a three-dimensional (3D) risk map and alerting a driverabout a potential risk surrounding the vehicle.

FIG. 1 illustrates an example of a suitable computing system 100 thatmay be used according to one or more illustrative embodiments. Thecomputing system 100 is only one example of a suitable computing systemand is not intended to suggest any limitation as to the scope of use orfunctionality contained in the present disclosure. The computing system100 should not be interpreted as having any dependency or requirementrelating to any one or combination of components shown in theillustrative computing system.

The present disclosure is operational with numerous other generalpurpose or special purpose computing systems or configurations. Examplesof well-known computing systems, environments, and/or configurationsthat may be suitable for use with the disclosed embodiments include, butare not limited to, personal computers (PCs), server computers,hand-held or laptop devices, mobile devices, tablets, multiprocessorsystems, microprocessor-based systems, set-top boxes, programmableconsumer electronics, network PCs, minicomputers, mainframe computers,distributed computing environments that include any of the above systemsor devices, and the like.

With reference to FIG. 1, the computing system 100 may include acomputing device 101 wherein the processes discussed herein may beimplemented. The computing device 101 may have a processor 103 forcontrolling the overall operation of the random access memory (RAM) 105,read-only memory (ROM) 107, input/output module 109, memory 115, modem127, and local area network (LAN) interface 123. Processor 103 and itsassociated components may allow the computing device 101 to run a seriesof computer readable instructions related to receiving, storing,generating, calculating, identifying, and analyzing data to generate arisk map. Computing system 100 may also include optical scanners (notshown). Exemplary usages include scanning and converting paperdocuments, such as correspondence, data, and the like to digital files.

Computing device 101 may include a variety of computer-readable media.Computer-readable media may be any available media that may be accessedby computing device 101 and include both volatile and non-volatile mediaas well as removable and non-removable media. Computer-readable mediamay be implemented in any method or technology for storage ofinformation such as computer-readable instructions, data structures,program modules, or other data. Computer-readable media include, but arenot limited to, random access memory (RAM), read only memory (ROM),electronically erasable programmable read only memory (EEPROM), flashmemory or other memory technology, or any other medium that can be usedto store desired information that can be accessed by computing device101. For example, computer-readable media may comprise a combination ofcomputer storage media (including non-transitory computer-readablemedia) and communication media.

RAM 105 may include one or more applications representing theapplication data stored in RAM 105 while the computing device 101 is onand corresponding software applications (e.g., software tasks) arerunning on the computing device 101.

Input/output module 109 may include a sensor(s), a keypad, a touchscreen, a microphone, and/or a stylus through which a user of computingdevice 101 may provide input, and may also include a speaker(s) forproviding audio output and a video display device for providing textual,audiovisual, and/or graphical output.

Software may be stored within memory 115 and/or storage to provideinstructions to processor 103 for enabling computing device 101 toperform various functions. For example, memory 115 may store softwareused by the computing device 101, such as an operation system 117,application program(s) 119, and an associated database 121. Also, someor all of the computer-executable instructions for computing device 101may be embodied in hardware or firmware.

Computing device 101 may operate in a networked environment supportingconnections to one or more remote computing devices, such as computingdevices 135, 141, and 151. The computing devices 141 and 151 may bepersonal computing devices, mobile computing devices, or servers thatinclude many or all of the elements described above about the computingdevice 101. The computing device 135 may be a transceiver or sensor thatincludes many or all of the elements described above about computingdevice 101.

The network connections depicted in FIG. 1 include a local area network(LAN) 125 and a wide area network (WAN) 129, but may also includeanother type of network. When used in a LAN networking environment,computing device (e.g. in some instances a server) 101 may be connectedto the LAN 125 through a network interface (e.g. LAN interface 123) oradapter in the communications module 109. When used in a WAN networkingenvironment, the computing device 101 may include a modem 127 or othermeans for establishing communications over the WAN 129, such as theInternet 131 or another type of computer network. It will be appreciatedthat the network connections shown are illustrative, and other means ofestablishing a communications link between the computing devices may beused. Various well-known protocols such as TCP/IP, Ethernet, FTP, HTTPand the like may be used, and the system may be operated in aclient-server configuration to permit a user to retrieve a web page froma web-based server. Further, various conventional web browsers may beused to display and manipulate web pages.

Various aspects described herein may be embodied as a method, a dataprocessing system, or as a computer-readable medium storingcomputer-executable instructions. For example, a computer-readablemedium may store instructions to cause a processor 103 to perform stepsof methods described herein. Such a processor 103 may executecomputer-executable instructions stored on a computer-readable medium.

FIG. 2 illustrates an example system in which sensors 204 are coupled toa vehicle 208. In other examples, only a single sensor 204 may be used.The sensors 204 may be coupled to vehicle 208 in the arrangement shownin FIG. 2 or in other various arrangements (not shown). During operationof the vehicle 208, a user (e.g., driver, passenger, etc.) of thevehicle 208 may be located at the position depicted by identifier 202.Sensors 204 (e.g. 204 a through 204 h) may be located inside, outside,on the front, on the rear/back, on the top, on the bottom, and/or oneach side of the vehicle 208. In some cases, the number of sensors 204used and positioning of the sensors 204 may depend on the vehicle 208,so that sensor information for all areas surrounding the vehicle 208 maybe collected.

The sensor(s) 204 may gather or detect sensor information. The sensorinformation may comprise data that represents the external surroundingsof the vehicle 208. In some examples, the sensor information may includedata that represents the vehicle 208 itself so that the vehicle's shapeand size may be determined from such data. The sensor(s) 204 maycomprise a light detection and ranging (LIDAR) sensor, a soundnavigation and ranging (SONAR) sensor, a video/image recording sensor, alight sensor, a thermal sensor, an optical sensor, an accelerationsensor, a vibration sensor, a motion sensor, a position sensor, a pointcloud sensor (e.g., for obtaining data to generate a point cloudfigure/object/image/etc.), a technology (e.g., sensing device orscanner) used to sense and detect the characteristics of the sensingdevice's surroundings and/or environment, and the like. In someembodiments, each sensor 204 a through 204 h may be the same type ofsensor. In other embodiments, sensors 204 a through 204 h may comprise acombination of different sensors. For example, sensor 204 a may be aLIDAR sensor, and sensor 204 b may be an optical sensor. In someembodiments, the sensors 204 may be specially designed to combinemultiple technologies (e.g., a sensor 204 may include accelerometer andLIDAR components). In some aspects, the sensor information may be in theform of vectors. The vectors may be labeled or organized based onclassification of each vector. The classification of each vector (and/orsets of vectors) may be generated using a formulaic or machine learningapproach. The information the vector contains, or more generally, thesensor information, may be stored, quantized, or interpreted with otherapproaches, e.g., graph data for semantic inference queries at a laterpoint in time.

The system may gather additional information, such as environmentalinformation, road information, vehicle information, weather information,geographic location information, accident information, etc.Environmental information may comprise data about the surroundings ofthe vehicle 208. In some embodiments, the environmental information maycomprise road, weather, and geographic information. For example,environmental information may comprise data about the type of route thevehicle 208 is traveling along (e.g., if the route is rural, city,residential, etc.). In another example, the environmental informationmay include data identifying the surroundings relative to the road beingtraveled by the vehicle 208 (e.g., animals, businesses, schools, houses,playgrounds, parks, etc.). As another example, the environmentalinformation may include data detailing foot traffic and other types oftraffic (e.g. pedestrians, cyclists, motorcyclists, and the like).

Road information may comprise data about the physical attributes of theroad (e.g., slope, pitch, surface type, grade, and the like). In someaspects, the physical attributes of the road may comprise a pothole(s),a slit(s), an oil slick(s), a speed bump(s), an elevation(s) orunevenness (e.g., if one lane of road is higher than the other, whichoften occurs when road work is being done), etc. In some embodiments,road information may comprise the physical conditions of the road (e.g.,flooded, wet, slick, icy, plowed, not plowed/snow covered, etc.). Insome instances, road information may be data from a sensor that gathersand/or analyzes some, most, or all vertical changes in a road. In otherexamples, road information may include information about characteristicscorresponding to the rules of the road or descriptions of the road:posted speed limit, construction area indicator (e.g., whether locationhas construction), topography type (e.g., flat, rolling hills, steephills, etc.), road type (e.g., residential, interstate, 4-lane separatedhighway, city street, country road, parking lot, etc.), road feature(e.g., intersection, gentle curve, blind curve, bridge, tunnel), numberof intersections, whether a roundabout is present, number of railroadcrossings, whether a passing zone is present, whether a merge ispresent, number of lanes, width of roads/lanes, population density,condition of road (e.g., new, worn, severely damaged with sink-holes,severely damaged by erosion, gravel, dirt, paved, etc.), wildlife area,state, county, and/or municipality. In some embodiments, roadinformation may include data about infrastructure features of the road.For example, infrastructure features may include intersections, bridges,tunnels, railroad crossings, and other roadway features.

Weather information may comprise data about the weather conditionsrelative to a vehicle's 208 location (e.g., snowing, raining, windy,sunny, dusk, dark, etc.). In some aspects, weather information mayinclude a forecast of potential weather conditions for a segment of aroad being traveled by vehicle 208. For example, weather information mayinclude a storm warning, a tornado warning, a flood warning, a hurricanewarning, etc. In some aspects, weather information may provide dataabout road segments affected by weather conditions. For example, weatherinformation may detail which roads are flooded, icy, slick,snow-covered, plowed, or closed. As another example, the weatherinformation may include data about glare, fog, and the like.

Vehicle information may comprise data about how the vehicle 208 isoperated (e.g., driving behavior). In some embodiments, a vehicletelematics device may be used to gather information about operation of avehicle. For example, the vehicle telematics device may gather dataabout the breaking, accelerating, speeding, and turning of a vehicle208. In some aspects, vehicle information may comprise accidentinformation (which will be described later). For example, vehicleinformation may include data that describes incidents (e.g., vehicleaccidents) and a particular location where the incident occurred (e.g.,geographic coordinates associated with a road segment, intersection,etc.). In some aspects, vehicle information may include the vehiclemake, vehicle model, vehicle year, and the like. In some instances,vehicle information may comprise data collected through one or morein-vehicle devices or systems such as an event data recorder (EDR),onboard diagnostic system, or global positioning satellite (GPS) device.Examples of information collected by such devices include speed atimpact, brakes applied, throttle position, direction at impact, and thelike. In some examples, vehicle information may also include informationabout the user (e.g., driver, passenger, and the like) associated withthe vehicle 208.

In some aspects, user information may include data about a user's age,gender, marital status, occupation, blood alcohol level, credit score,eyesight (e.g., whether the user wears glasses and/or glassesprescription strength), height, and physical disability or impairment.In some instances, user information may include data about the user'sdistance from a destination, route of travel (e.g., start destinationand end destination), and the like. In some embodiments, the userinformation may comprise data about the user's non-operation activitieswhile operating a vehicle 208. For example, the data may comprise theuser's mobile phone usage while operating the vehicle 208 (e.g., whetherthe user was talking on a mobile device, texting on a mobile device,searching on the internet on a mobile device, etc.), the number ofoccupants in the vehicle 208, the time of day the user was operating thevehicle 208, etc.

Geographic location information may comprise data about the physicallocation of a vehicle 208. For example, the geographic locationinformation may comprise coordinates with the longitude and latitude ofthe vehicle, or a determination of the closest address to the actuallocation of the vehicle 208. In another example, the vehicle locationdata may comprise trip data indicating a route the vehicle 208 istraveling along. In some aspects, the geographic location informationmay also include information that describes the geographic boundaries,for example, of an intersection (e.g. where vehicle 208 is located)which includes all information that is associated within a circular areadefined by the coordinates of the center of the intersection and pointswithin a specified radius of the center. In some embodiments, geographiclocation information may consist of numerous alternative routes avehicle 208 may travel to reach a selected destination.

Accident information may comprise information about whether a vehicle208 was in an accident. In some aspects, accident information mayidentify damaged parts of the vehicle 208 resulting from the accident.For example, accident information may detail that the front bumper,right door, and right front headlight of the vehicle 208 were damaged inan accident. In some examples, accident information may detail the costof replacement or repair of each part damaged in an accident. In someinstances, accident information may include previously described vehicleinformation. In some embodiments, accident information may include dataabout the location of the accident with respect to a road segment wherethe accident occurred. For example, accident information may includewhere the accident occurred on the road segment (e.g., which lane), thetype of road the accident occurred on (e.g., highway, dirt, one-way,etc.), time of day the accident occurred (e.g., daytime, night time,rush hour, etc.), and the like.

Some additional examples of accident information may include loss type,applicable insurance coverage(s) (e.g., bodily injury, property damage,medical/personal injury protection, collision, comprehensive, rentalreimbursement, towing), loss cost, number of distinct accidents for thesegment, time relevancy validation, cause of loss (e.g., turned leftinto oncoming traffic, ran through red light, rear-ended whileattempting to stop, rear-ended while changing lanes, sideswiped duringnormal driving, sideswiped while changing lanes, accident caused by tirefailure (e.g., blow-out), accident caused by other malfunction of car,rolled over, caught on fire or exploded, immersed into a body of wateror liquid, unknown, etc.), impact type (e.g., collision with anotherautomobile, collision with cyclist, collision with pedestrian, collisionwith animal, collision with parked car, etc.), drugs or alcoholinvolved, pedestrian involved, wildlife involved, type of wildlifeinvolved, speed of vehicle at time of incident, direction the vehicle istraveling immediately before the incident occurred, date of incident,time of day, night/day indicator (i.e., whether it was night or day atthe time of the incident), temperature at time of incident, weatherconditions at time of incident (e.g., sunny, downpour rain, light rain,snow, fog, ice, sleet, hail, wind, hurricane, etc.), road conditions attime of incident (e.g., wet pavement, dry pavement, etc.), and location(e.g., geographic coordinates, closest address, zip code, etc.) ofvehicle at time of incident.

Accident information associated with vehicle accidents may be stored ina database format and may be compiled per road segment and/or risk map.One skilled in the art will understand that the term road segment may beused to describe a stretch of road between two points as well as anintersection, roundabout, bridge, tunnel, ramp, parking lot, railroadcrossing, or other feature that a vehicle 208 may encounter along aroute.

FIG. 3 illustrates a computing system 300 for generating a risk mapbased on sensor information. Computing system 300 may include risk mapgeneration system 302, sensor(s) 304, mobile computing device 306, andvehicle 308. In some embodiments, computing device 101 may comprise therisk map generation system 302. In other embodiments, risk mapgeneration system 302 may comprise or be similar to the previouslydescribed computing devices 101, 141, or 151. In some aspects, sensor(s)304 may be similar to the previously described sensor(s) 204. Forexample, sensor(s) 304 may be coupled to a vehicle 308. In someinstances, vehicle 308 may be similar to the previously describedvehicle 208. In some examples, a user mobile computing device 306 may besimilar to the previously described computing devices 101, 141, or 151.

The risk map generation system 302 may receive sensor information andutilize the information to complete different tasks. For example, therisk map generation system 302 may receive sensor information fromsensor(s) 304, and communicate with (e.g., transmit to and receive from)mobile computing device 306 in order to generate a risk map. In anotherexample, the risk map generation system 302 may use received informationand risk map generation system's modules to develop alerts that areincluded within the risk map, which may help to alert a driver ofpotential risks. A risk (e.g., potential risk) may comprise anythingthat may create a dangerous driving condition or increase a likelihoodof a vehicle 308 getting into an accident. A risk map may comprise animage (e.g., JPEG, TIFF, BMP, etc.), a video (e.g., MPEG), a hologram,or other visual outputs for illustrating a road segment or route beingtraveled by a vehicle 308. The risk map may further include markers orother indicators of risks (e.g. risk objects). Risks may be any item,event, or condition that may pose a danger to a vehicle 308 while thevehicle 308 is on a trip. In various embodiments, the risk map may be atwo-dimensional (2D) or a three-dimensional (3D) illustration. Further,in some embodiments, the risk map may dynamically change over time. Thechanges may be in accordance with geographic data indicating thevehicle's location and/or other data (e.g., speed data indicating aspeed of the vehicle or odometer data indicating distance the vehicletraveled). In some embodiments, the risk map may be keyed or coded(e.g., certain symbols, colors, and the like that represent differentrisks or categorize the risk objects within the risk map).

In some embodiments, a risk map generation system 302 may createdifferent risk maps for different devices or different users. Forexample, one risk map may be generated for a user of a vehicle 308 whilea different risk map may be generated for a different user of anothervehicle. The differences in the risk maps may depend on the past drivingbehavior of the different users (e.g., drivers) and may take intoaccount that different things may pose different risks to differentusers. Although risk maps are often described herein as being displayedto drivers of a vehicle, it should be understood that risk maps may begenerated for and displayed to pedestrians, joggers, runners, bikeriders, motorcyclists, and the like. As another example, a risk map maybe generated for a commercial truck driver. Under this example,different risk objects may be highlighted on the risk map such as knownclearances, hanging power lines, and the like. In some embodiments, arisk map may be created for coordinating risk inside a building. Forexample, a risk map may be created to help a pedestrian navigate theirway through a mall or an airport. In some instances, a risk mapgeneration system 302 may generate a risk map that includes risk objectsbased on historical data. Historical data may comprise information aboutthe prevalence of risk objects on a particular road segment over a givenperiod of time. For example, a risk map may include risk objects basedon where future risk may be located based on historical data or whererisk is historically located on a road segment. In some aspects, a riskmap generation system 302 may create a risk map based on pre-determinedroad segment information. For example, the risk map generation system302 may receive road segment information for a segment of road a vehicle308 is traveling on, and use the received road segment information togenerate a risk map of the road segment. In some instances, a risk mapgeneration system 302 may receive one or more risk maps from anothercomputing device, identify which particular risk map of the one or morerisk maps matches the segment of road a vehicle 308 may be traveling on,and generate a new risk map using the identified particular risk mapalong with sensor information obtained by the vehicle 308. In someembodiments, a risk map generation system 302 may create a risk mapbased on risk (e.g., risk objects). In some aspects, a risk mapgeneration system 302 may create a risk map which provides differentroutes to a user to mitigate risk. For example, a generated risk map maycontain different routes of travel based on the road segments a user maytravel to arrive at their end destination. Under this example, eachroute may correlate to a different risk value based on the number andthe type of risk objects located on each route.

In some aspects, the risk map generation system 302 may display a riskmap to a user. In some examples, the risk map may be displayed on theexterior of the vehicle 308 (e.g., on the hood of a vehicle 308), on theinterior of the vehicle 308 (e.g., on a display device, LCD screen, LEDscreen, and the like), or on the windshield of the vehicle 308 (e.g.,heads-up display [HUD]). In some embodiments, a risk map may bedisplayed as a hologram, or on augmented reality (AR) glasses, or thelike.

The risk map generation system 302 may comprise various modules forgenerating a risk map. For example, a risk map generation system 302 mayinclude a data retrieval engine 310, an insurance rules processingengine 312, a user notification engine 314, an information managementsystem 316, a point cloud engine 318, and a map generation engine 320.

The data retrieval engine 310 may request or receive information fromother computing devices and sensors. For example, a data retrievalengine 310 may receive sensor information from sensor(s) 304, data frommobile computing device 306, and/or instructions/data from a user deviceor network device (not shown). A data retrieval engine 310 may receivedifferent types of sensor information as those previously described. Forexample, a data retrieval engine 310 may obtain environmentalinformation, vehicle information, weather information, and the like. Insome aspects, a data retrieval engine 310 may receive and use the sensorinformation (e.g., x-plane information, y-plane information, and z-planeinformation) to determine whether a vehicle 308 is moving up or down.

The insurance rules processing engine 312 may receive and store dataand/or instructions from an insurance provider on how to determine whatposes a risk to a driver of a vehicle 308 (e.g. identify a risk object).In some instances, an insurance rules processing engine 312 may includea risk processing module for determining a risk value for a potentialrisk (e.g., risk object). For example, the risk processing module mayevaluate a risk object and assign it a risk value. As another example,the risk processing module may assign a certain risk value to a roadsegment that is wet from rain, and assign a lower risk value to the roadsegment that is not wet from rain. In some embodiments, the riskprocessing module may calculate the risk value for a road segment, riskobject, or point of risk by applying actuarial techniques. In someaspects, an insurance rules processing engine 312 may determine how toidentify or present a risk object to a driver. In some aspects, aninsurance rules processing engine 312 may process insurance policyinformation related to the user. For example, the insurance rulesprocessing engine 312 may update a user's insurance information, adjustthe user's insurance premium, adjust the user's insurance coverage, filea claim, or complete any other insurance task or process.

The user notification engine 314 may generate an alert on the risk mapto help the user identify an upcoming and potential risk(s) on theirroute of travel. In some aspects, the user notification engine 314 maydetermine how to display a risk object to a user via the risk map. Insome instances, a user notification engine 314 may generate alerts thatmay be provided to a user about adjustments to their insurance. In someembodiments, a user notification engine 314 may generate an insuranceclaim. In some aspects, a user notification engine 314 may develop riskvalues (e.g. a risk score), based on the sensor information. Forexample, a risk score may be a value associated with a particular roadsegment that is flat, and the risk score may be increased due to rainmaking the road wet. Under this example, the user notification engine314 may assign a new risk score the road segment and notify the userthat the risk score has changed. In some examples, a risk score mayrelate to a risk object being displayed in the risk map, and the riskscore may alter the presentation of the risk object within the risk mapto indicate a certain level of risk associated with the risk object. Forexample, if there is a pothole on the road, the risk map may display thepothole in a particular color or the pothole may be blinking on the riskmap. In some embodiments, a risk object may be enhanced with anindicator which may be associated with a risk ranking system. A riskranking system may perform a method for prioritizing or labeling thedifferent levels of risk or potential trouble/danger associated with arisk object.

In some aspects, the indicator may be a color, an animation, a sound, avibration, and the like for indicating the level of risk associated witha risk object. For example, a pothole may be displayed in yellow if itis a moderate risk to a vehicle 308 or displayed in red if it is asevere risk to the vehicle 308. In another example, one sound may beplayed for a low risk while a different sound may be played for a highrisk. In another example, if a risk object is identified as beinglocated at the front of the vehicle 308, then the floor or seat of thevehicle 308 may vibrate. In another example, if a risk object isidentified as being located at the back/rear of the vehicle 308, thenthe back of the seat(s) of the vehicle 308 may vibrate. In someexamples, if a risk object is identified as being located on the leftside of the vehicle 308, then a sound may play out of the leftspeaker(s) of the vehicle 308. In some examples, if a risk object isidentified as being located on the right side of the vehicle 308, then asound may play out of the right speaker(s) of the vehicle 308.

Information management system 316 may organize and store all theinformation the risk map generation system 302 generates, transmits, andreceives. In some aspects, the information management system 316 maystore risk maps. In some instances, the information management system316 may include a database for storing risk values associated with riskobjects or road segments, or for storing risk values, risk objects, orroad segments. In some embodiments, the information management system316 may store routes (e.g., route information), risk objects (e.g., riskobject information), and risk maps (e.g., risk map information) fromother computing devices.

The point cloud engine 318 may generate point cloud information based onthe sensor information received by the risk map generation system 302.For example, the point cloud engine 318 may generate point cloudinformation when the sensor(s) 304 sends signals in every direction, andthe signals hit an object(s) and bounces back. In some examples, thepoint cloud engine 318 may be able to determine what an object is, fromreceiving a few data points from a signal that has hit an object. Insome embodiments, the point cloud engine 318 may generate data todevelop a risk map that simulates vehicle 308 traveling through a pointcloud. In some aspects, a sensor(s) 304 may obtain video data, and thepoint cloud engine 318 may map a point cloud from the video data. Insome instances, the point cloud engine 318 may obtain point cloudinformation using different frequencies of data collection. In someembodiments, the point cloud engine 318 may create point cloud imageswhich may be used to generate a risk map. In some aspects, the pointcloud engine 318 may generate point cloud images or figures that may beused to represent risk objects. In some instances, the point cloudimages or figures may be displayed within the risk map. In someembodiments, the point cloud engine 318 may generate images or figuresthat are scaled (e.g., the scaling may be accurate to a centimeter). Insome aspects, the point cloud engine 318 may obtain sensor informationto generate figures represented by data points that may be mapped to acoordinate system (e.g., X, Y, and Z coordinates). In some embodiments,the point cloud engine 318 may create point clouds using athree-dimensional scanner. In some aspects, point cloud images may begathered at the same time that video sensor data may be acquired. Thevideo sensor data may include video stills (e.g., framing) that may bemapped to a point cloud, which may act as a mesh for holding an image.Any of the various programs and tools for mapping video to LIDARgathering point clouds may be used.

The map generation engine 320 may collaborate with the other previouslymentioned engines and systems to develop a risk map. In some aspects,the map generation engine 320 may output or display a risk map that maycomprise information about the environmental surroundings of a vehicle308 and the risks associated with the surroundings of the vehicle 308 asthe vehicle 308 travels along a road segment. For example, the risk mapmay include the road segment a vehicle 308 is traveling on, along withthe characteristics of the road segment, e.g., the trees, the buildings,and the weather conditions of the environment encompassing the roadsegment. In some examples, the map generation engine 320 may develop athree-dimensional and/or virtual world risk map. In some embodiments,the map generation engine 320 may retrieve GPS data and combine the GPSdata with data from other engines and systems of the risk map generationsystem 302 to develop a risk map. In some embodiments, the risk map,which the map generation engine 320 creates, may not reflect reality(e.g., the risk map may be distorted). In some instances, a mapgeneration engine 320 may assemble a risk map that augments reality inorder to show a visual representation of the vehicle's 308 environment.

The engines/modules/systems 302, 310, 312, 314, 316, 318, and 320 may,individually or in combination, comprise one or more processors andmemory storage devices (as previously described with reference toFIG. 1) for executing instructions causing one or more computing devicesto execute the operations and/or tasks previously described with respectto items 302, 310, 312, 314, 316, 318, and 320.

FIG. 4 illustrates a user interface 400. The user interface 400 may bedisplayed to a user and may illustrate aspects of a risk map. The userinterface 400 may comprise images or figures. For example, the userinterface 400 may include images of a pedestrian 402, an oil slick 404,a pothole 406, a vehicle(s) 408, and/or an animal 410. In some aspects,FIGS. 402, 404, 406, 408, and 410 may be point cloud images. In someinstances, the interface 400 may be a point cloud interface. In someaspects, these images may indicate a risk of the road segment or routebeing travel by a vehicle(s) 408. In some examples, the location ofthese images within the user interface 400 may be determined based onthe sensor information received by risk map generation system 302.

In some aspects, the user interface 400 may be displayed in aperspective view of the user (not shown) (e.g., the same view a user maysee while operating a vehicle 408). In some examples, the user interface400 may display all risk objects relative to a vehicle (e.g., in front,in back, on the either side, below, and above the vehicle 408). Althoughthe user interface 400 may be shown as a two-dimensional image in FIG.4, in some embodiments, the user interface 400 may be three-dimensional.In other embodiments, the user interface 400 may be displayed on theinside or outside of the vehicle(s) 408. For example, the user interface400 may be displayed on the windshield of the vehicle(s) 408 or on thehood of the vehicle(s) 408. In some examples, the user interface 400 maybe a heads-up display unit (HUD). In some examples, the user interface400 may be displayed on a computing device located on the interior ofthe vehicle 408 (e.g., on a screen on the dashboard of the vehicle or ona screen of a smartphone of the driver or passenger in the vehicle). Theuser interface 400 may be configured for display on various displayscreens (e.g., LED screen, LCD screen, plasma screen, holographicdisplay, and the like). In some aspects, user interface 400 may displaythe 3D risk map by displaying images on the mesh of a 3D space, or as asign (e.g., tip, waring, compliments, etc.) in a point cloud augmentinga 3D view (for example, like a sign that pops out of the ground or asemojis). These tips, warnings, compliments may be different in color,use animation, flashing, or appear in a 3D space on a heads-up 2D or 3Ddisplay. For example, a sign that reads “Deer!” may appear within the 3Drisk map above an image of a deer (or an emoji of a deer) within the 3Drisk map. Other techniques for updating and displaying a 3D risk map orobjects within the 3D risk map may be similar to images and figureswhere photoshop filters are used (e.g., blurring what doesn't matter,sharpening what does matter, and adjusting contrast/brightness on visualdata). In some examples, expressing a risk object in a 3D environmentmay be haptic feedback from a handset, using focused audio to express amessage (like bass going through a port), puffs of warm or cold air, andair with different scents.

In some embodiments, 3D printers may be used for reconstructingaccidents or simulations. In some cases, an insurance provider may usethe reconstructed accidents or simulations generated by the 3D printerto determine how to process an insurance claim. In some instances, thereconstructed accidents or simulations generated by the 3D printer maybe supplied to drivers of vehicles that may have been in the accidents.

FIG. 5 illustrates examples of point cloud images. Such point cloudimages may appear on a risk map. In some aspects, the images on a riskmap may be point cloud representations of items deemed to pose a risk tothe driver and/or vehicle. By way of example, FIG. 5 illustrates pointcloud representations of several objects that may pose a risk: a pointcloud representation of a vehicle 508, a point cloud representation of apedestrian 502, and a point cloud representation of an animal (e.g.,bunny) 510. Such point cloud images may appear in a user interface(e.g., user interface 400). The point cloud images may represent riskslocated on or around the road segment or route a vehicle is traveling.For example, a pedestrian or animal may be on the side of a road onwhich the vehicle is traveling, and thus, the point cloud representationof the pedestrian 502 and/or point cloud representation of the animal510 may appear along the road segment within the risk map. In someexamples, the positioning of these point cloud representations maycorrespond to their actual positions with respect to the road thevehicle is traveling on. In some aspects, the point cloud risk objectsmay be color-coded or keyed to alert the driver of a potential risk andthe severity of the potential risk. For example, pedestrian 502 may bedisplayed in red within the risk map to indicate there may be a highrisk the pedestrian may get in the vehicle's way, while animal 510 maybe displayed in yellow indicating there may be a moderate risk theanimal may get in the vehicle's way. Objects may be deemed to be highrisk depending on the amount of damage they might cause a vehicle,because of their size or type (e.g., a deer may cause more damage than asquirrel so a deer may be displayed in a way to indicate that it poses ahigher risk), or depending on the likelihood they might contact thevehicle (e.g., a deer might be more likely to hit a vehicle than apedestrian who would use common sense to stay out of the path of thevehicle). Distance from the road may also contribute to the level ofrisk associated with an item. For example, an animal closer to the roadmay be displayed as posing a higher risk than an animal farther from thevehicle. In some embodiments, the risk objects may be linked to a soundor vibration to alert the user of a potential risk. For example, if apedestrian 502 appears on the risk map, then a beeping sound may playover the vehicle's speakers to indicate a high risk. As another example,if an animal 510 appears on the risk map, then the driver's seat or thesteering wheel of the vehicle may vibrate.

FIG. 6 illustrates a method for generating a risk map and an alert thatmay be provided to a user. The method may begin at step 605. At step605, a risk map generation system 302 may receive sensor informationfrom various sensors (e.g. sensor(s) 304). In some aspects, the risk mapgeneration system 302 may store the received sensor information. In someexamples, the sensor information may be the same as the sensorinformation previously described. In some embodiments, the risk mapgeneration system 302 may receive sensor information from mobilecomputing device 306, a network device, or other computing devices.After step 605, the method may proceed to step 610.

At step 610, a risk map generation system 302 may receive geographicinformation (e.g., geographic location information). In someembodiments, the risk map generation system 302 may receive thegeographic location information as part of the sensor information. Insome aspects, the risk map generation system 302 may receive thegeographic location information from a GPS device installed in avehicle, mobile computing device 306, or another computing device. Forexample, in step 610, the risk map generation system 302 may receive GPScoordinates from a mobile phone of a driver of a vehicle via a cellularbackhaul. In some instances, the geographic location information maycomprise the same geographic location information as previouslydescribed. For example, the geographic location may comprise latitudeand longitude coordinates of vehicle 308. After step 610, the method mayproceed to step 615.

At step 615, the risk map generation system 302 may receiveenvironmental information. In some embodiments, the risk map generationsystem 302 may receive the environmental information from a mobilecomputing device 306 or another computing device located in or on thevehicle. Additionally, or alternatively, the risk map generation system302 may receive environmental information from a network device (e.g., athird party server, such as a server of a weather reportingorganization). In some instances, the network device may be a computingdevice or a server similar to the risk map generation system 302, andmay be operated by an insurance company. In some examples, the networkdevice may contain environmental information about various routes,various road segments, various risk objects, and/or various risk mapsthat may be related to a route that a vehicle 308 may be travelingalong. In some instances, the environmental information may comprisedata about routes, road segments, risk objects, and/or risk maps. Insome examples, the environmental information may be similar toenvironmental information previously described. In other embodiments,the risk map generation system 302 may receive environmental informationfrom another computing device (system) associated with a differentvehicle. In another embodiment, the risk map generation system 302 mayreceive environmental information regarding a road segment, via acomputing device responsible for tracking the conditions of the roadsegment. In some embodiments, the risk map generation system 302 mayreceive environmental information from a structure/infrastructure (e.g.building, bridge, railroad track, etc.) via a computing deviceconfigured to monitor the structure. After step 615, the method mayproceed to step 620.

At step 620, the risk map generation system 302 may generate a risk mapbased on the received information, individually or combined, from steps605, 615, and 620. In some embodiments, the risk map generation system302 may generate a risk map using sensor information. In some aspects,the risk map generation system 302 may analyze and manipulate the sensorinformation in order to create images, figures, and the like thatrepresent a map or an environment that a vehicle 308 may be travelingthrough. In some instances, the manipulation of sensor information maycomprise interpreting input information (e.g., sensor information) andreconstructing the sensor information in a way it may be used togenerate an image, an object, or a virtual environment. In someembodiments, the risk map generation system 302 may convert the sensor,geographic location, and/or environmental information into point cloudinformation that may be used to create a point cloud map or a pointcloud figure. In some examples, the point cloud figure or map may beused to generate a virtual world that resembles the environment avehicle 308 may be traveling through. The risk map generation system 302may generate a risk map using one or more point clouds representingobjects (e.g. pedestrians, animals, buildings, etc.). In someembodiments, the risk map generation system 302 may generate a risk mapby superimposing point cloud images into a virtual world. In someaspects, the risk map generation system 302 may generate a risk mapusing risk objects, risk values, road segments, and/or other risk maps.In some embodiments, risk map generation system 302 may generate a riskmap using road segments, risk objects, and risk maps received from othercomputing devices. In some aspects, risk map generation system 302 maygenerate the risk map using sensor information and/or receivedinformation (e.g., data received by the risk map generation system 302from other devices). For example, the risk map generation system 302 maygenerate a risk map using only the sensor information related to thephysical attributes of the road segment being traveled by the vehicle308. As another example, the risk map generation system 302 may generatea risk map using physical attributes of a road segment the vehicle 308is traveling on combined with environmental information of the roadsegment, such as information indicating the current weather. Forexample, if it is raining the risk map may depict rain drops. In someexamples, the heavier the rain, the more rain drops there are depictedin the risk map. In view of this disclosure, it will be understood thatthe risk map may be generated in various ways. After step 620, themethod may proceed to step 625.

At step 625, the risk map generation system 302 may analyze the risk mapand calculate a risk value. In some aspects, the risk value may berepresented by a risk score. In some embodiments, the risk mapgeneration system 302 may calculate the risk value for a road segment,risk object, or point of risk by applying actuarial techniques to thesensor information that may be received from sensors 304. In otherexamples, risk map generation system 302 may calculate the risk value byapplying actuarial techniques to the information that may be received bythe risk map generation system 302. In some embodiments, the risk mapgeneration system 302 may calculate the risk value based on thelikelihood of a risk object causing the vehicle to get in an accident.In some aspects, the risk map generation system 302 may calculate therisk value using only road information. For example, the risk value maybe calculated based on only the physical attributes of the road. In someembodiments, the risk map generation system 302 may calculate the riskvalue using only the sensor information. In some instances, risk mapgeneration system 302 may analyze a risk map to calculate a risk valueas previously described with regards to FIG. 3.

After calculating a risk value at step 625, the risk map generationsystem 302 may associate the road segment a vehicle is traveling on tothe risk value at step 630. In some aspects, associating the risk valuemay include analyzing the road segment or risk map the vehicle 308 istraveling along or through, identifying one or more risk objects and thecharacteristics of the one or more risk objects, and calculating a riskvalue based on the number of risk objects and the characteristics of theone or more risk objects located on the road segment or within the riskmap. In some aspects, the risk map generation system 302 may have a listof pre-determined risk objects correlated to a predetermine risk value.In some aspects, the risk map generation system 302 may have differentgroupings of risk objects (which may be categorized by thecharacteristics of the risk objects) correlated to predetermined values.In some embodiments, a user may be able to determine, categorize, orcorrelate risk objects to a user selected value. In some embodiments, aspecially configured or programmed server or computing device of aninsurance provider that manages the computing system may rank,prioritize, or correlate the risk objects to a selected value. Forexample, all risk objects located on the side of the road may have arisk value of 10, while all risk objects located on the road may have arisk value of 20. In some aspects, the risk value assigned may representthe likelihood of a risk object causing an accident. For example, apothole with a 2 ft diameter may get a higher risk value than a pot holewith a 1 ft diameter. After step 630, the method may proceed to step635.

At step 635, the risk map generation system 302 may assemble risk datainto multivariable equations. For example, the risk map generationsystem 302 may use the data (identified at step 630) to determine a riskvalue of an object or a segment of road on the risk map based onpredetermined equations. The equations may be configured for differentinformation inputs which may affect the risk value assigned to a riskobject, risk map, and/or road segment. For example, one equation may usesensor data while another equation may use the sensor data,environmental data, and geographic location data to determine a riskvalue. In some instances, a network device or insurance provider'sserver may generate and determine the multivariable equations. In someembodiments, the multivariable equations may be generated usingactuarial techniques. Once the risk map generation system 302 assemblesthe received information into multivariable equations, the method mayproceed to step 640.

At step 640, the risk map generation system 302 may calculate a modifiedrisk value based on environmental information and/or other receivedinformation. For example, the risk map generation system 302 may use thedetermined risk values from step 625 and use the multivariable equationfrom step 635 to use additional received data (e.g., geographic locationinformation and/or environmental information) to calculate a modifiedrisk value. As another example, a risk value determined at step 625 maybe adjusted. Under this example, the risk map generation system 302 mayadjust a risk value due to a new condition (e.g. snow on the road). Dueto the snow, risk map generation system 302 may use the multivariableequation to determine that the previous risk value needs to be modified.Upon completion of step 640, the method may proceed to step 645.

At step 645, the risk map generation system 302 may store the modifiedrisk value in memory. In some aspects, the modified risk value may becorrelated to mark or enhance a particular risk object, road segment,and/or risk map. The particular risk object, road segment, or risk mapmay be updated and assigned the new modified risk value. The updatedrisk object, road segment, or risk map with its updated risk value maybe stored by the risk map generation system 302 into a database. In someaspects, the database information may be shared with other computingdevices or be used to generate other risk maps with similar road segmentcharacteristics. After step 645 is completed, the method may proceed tostep 650.

At step 650, the risk map generation system 302 may determine if therisk value (e.g. risk score) is over a threshold. The threshold valuemay be determined by a user or a system provider (e.g., insurancecompany/provider). Also, the threshold value may be adjusted on acase-by-case basis (e.g., driver by driver basis), or may bepredetermined. If the risk map generation system 302 determines that therisk value is not over the threshold, the method may proceed to step605. If the risk map generation system 302 determines that the riskvalue has exceeded the threshold, then the method may proceed to step655.

At step 655, the risk map generation system 302 may update the risk mapbased on the modified risk value. In some aspects, updating the risk mapmay comprise marking risk objects within the risk map with indicators.The indicators may function to help alert the driver of potential risks.In some aspects, updating the risk map may comprise adding risk objectsto or removing risk objects from the risk map. In some embodiments, theindicator may comprise color-coding the risk map. In some examples, theindicator may result in enhancing a risk object with a color, a sound,or an animation. In some instances, an indicator is similar to theindicator as described with reference to FIG. 3. After step 655 hascompleted, the method may proceed to step 660.

At step 660, the risk map generation system 302 may alert the user of avehicle. The alert may be an indicator or enhancement to a risk object,road segment, and/or risk map. The alert may be any previously describedindicator (with reference to FIG. 3). In some aspects, the alert mayidentify or indicate to the user of a vehicle that there is a potentialrisk and what that potential risk may be. In some examples, the risk mapgeneration system 302 may display the risk object, road segment, and/orrisk map to a user. In other examples, risk map generation system 302may provide the user with an audio alert. After step 660 has completed,the method of FIG. 6 may be repeated so that another risk map may begenerated or so that the risk map is continually and/or dynamicallyupdated to provide a virtual world representation of the surroundings ofthe vehicle as the vehicle moves.

FIG. 7 illustrates a method for generating a risk map and determining anaction to be taken based on the generated risk map. The method may beginat step 705. At step 705, the risk map generation system 302 may receivesensor information as previously described with reference to FIG. 6.Once step 705 has been completed, the method may proceed to step 710. Atstep 710, risk map generation system 302 may receive additional sensordata from another computing device associated with another vehicle. Forexample, the risk map generation system 302 receiving sensor dataassociated with the vehicle 308 may receive additional sensor data fromanother computing device associated with a different vehicle (differentfrom vehicle 308). As another example, the risk map generation system302 may download the additional sensor information from a networkdevice. In some embodiments, the risk map generation system 302 mayreceive additional sensor data from a plurality of different computingdevices respectively associated with a plurality of different vehicles.In some aspects, the additional sensor data may comprise a risk mapcontaining shell map data (e.g., a risk map with only road information),a baseline template of a risk map for a road segment, or the like. Insome aspects, the baseline template may include a risk map with a riskvalue for a road segment or a risk object. In some instances, thenetwork server may be owned and operated by an insurance provider. Insome embodiments, additional sensor data may comprise risk objects, roadsegments, or risk maps. In some examples, the additional sensor data maycomprise any of the previously described information. After step 710 hascompleted, the method may proceed to step 715.

At step 715, the risk map generation system 302 may generate a new(e.g., updated) risk map based on the received sensor data and theadditional sensor data. In some aspects, the risk map generation system302 may generate a new (e.g., updated) risk map by using an existingrisk map from the additional sensor data and updating the existing riskmap with the new sensor information obtained in step 705. The risk mapmay be generated in various manners as disclosed herein. In someembodiments, the risk map generation system 302 may generate athree-dimensional (3D) point cloud risk map or a two-dimensional mapwith 3D point cloud images overlaying the map. After step 715 hascompleted, the method may proceed to step 720.

At step 720, the computing device may analyze the risk map to calculatea risk value as previously described (e.g., with reference to FIGS. 3and 6). Once step 720 has completed, the method may proceed to step 725.At step 725, the risk map generation system 302 may update the risk mapbased on the risk value from step 720. In some aspects, risk mapgeneration system 302 may analyze the risk map by pattern matching. Forexample, pattern matching may comprise comparing the risk map based onthe additional sensor data at step 710 with a risk map generated usingthe sensor information at step 705. As another example, pattern matchingmay comprise comparing sensor information from step 705 with additionalsensor data received in step 710. In some embodiments, the updated riskmap may determine and provide alternative routes for a user to travelthat contain less risk (e.g. a lower risk value may be associated withthe alternative route). In some embodiments, the risk map generationsystem 302 may display the updated risk map to a user. After completionof step 725, the method may proceed to step 730.

At step 730, the risk map generation system 302 may use the updated riskmap to complete insurance tasks. For example, the updated risk map maybe used to adjust a user's insurance premium, modify a user's insurancecoverage, file a claim, pay a claim, report or record an accident, offerdifferent rates for insurance based on routes traveled, etc. In someaspects, a risk map generation system 302 may use the updated risk mapto offer a user a new or alternative route for traveling to theirdestination. For example, the risk map generation system 302 may offerdifferent routes based on risk values to the user in order for the userto reach their destination. In some embodiments, a risk map generationsystem 302 may provide the updated risk map to notify pedestrians,motorcyclist, cyclist, and the like. In some examples, a risk mapgeneration system 302 may use the updated risk map to notify emergencyresponders of potential risks on particular roads. The risk mapgeneration system 302 may be able to provide first responders with aplurality of possible routes to a predetermined destination and rankthem based on risk value, time, distance, traffic, and other safety andtravel factors. In other examples, a risk map generation system 302 mayuse the updated risk map to influence autonomous and semi-autonomousvehicles. For example, the risk map generation system 302 maycollaborate with a vehicle's systems in order to slow the vehicle downor to help the vehicle avoid an accident, based on the updated risk map.

In some aspects, users may be assigned a consumption score based on theupdated risk maps. A consumption score may be a value correlated to auser's exposure to risk while operating a vehicle. In some instances,the updated risk map may be used to update the consumption score. Insome examples, the consumption score may be affected by the route theuser chooses to take. In some aspects, the consumption score may be usedto characterize a user. For example, a user with a low consumptionscore, meaning they choose to travel safer routes, may be less likely tocommit fraud. On the other hand, a user with a high consumption score,meaning they choose to travel more hazardous routes, may be more likelyto commit fraud. In another example, the consumption score may be ableto be used in place of a credit score.

In some embodiments, the updated risk map may be used by a paid driver,collaborative driver, or delivery driver (e.g., taxi, ride-share,package delivery service, etc.) Under these examples, the updated riskmap may affect the price of the services. For example, a taxi driver mayadjust his fare, based on the updated risk map, if a service userselects a route with a higher risk value than an alternative route. Asanother example, a delivery service may charge less for their servicesif their drivers travel the safest routes to their destinations, basedon the updated risk map. In some embodiments, paid drivers may be ableto offer pricing by route, based on the updated risk map. In someinstances, a user may be able to select a paid driver based on thedriver's consumption score.

FIGS. 8A-8C illustrate a method for using the risk map to control howinsurance claims may be processed. The method may begin at step 802 uponpower up of a computing device (e.g., a mobile computing device within avehicle or a server) or upon detection (e.g., by a computing devicewithin the vehicle) that the vehicle is starting a trip or has started atrip. For example, the method may proceed to step 804 when it isdetermined that the vehicle is being driven or will be driven shortly(which may be determined by determining that the vehicle left a home,the vehicle has exceeded a certain speed, or the vehicle's engine hasturned on). At step 804, the risk map generation system 302 may receivesensor information, as previously described. Upon completion of step804, the method may move to step 806. At step 806, the risk mapgeneration system 302 may receive environment information, as previouslydescribed. Following step 806, the method may continue to step 808.

At step 808 of the method, a risk map generation system 302 maydetermine a risk value for a segment of road a vehicle is travelingbased on the received information. In some embodiments, a risk mapgeneration system 302 may determine a risk value by analyzing the sensorinformation or environment information to identify one or more riskobjects, analyzing the one or more risk objects, and determining a riskvalue based on the one or more risk objects. In some embodiments, therisk map generation system 302 may link the risk value(s) of the one ormore risk objects to a particular road segment or risk map. For example,the risk map generation system 302 may receive sensor information andidentify a pothole on the road. The risk map generation system 302 mayevaluate the size, depth, and location of the pothole and determine arisk value for the road segment, based on that road segment containingthe pothole. As another example, the risk map generation system 302 mayreceive sensor information and environment information, identify snow onthe road and a pedestrian in a crosswalk, and determine a risk value forthe road segment. In other examples, a risk map generation system 302may download a risk map for a road segment from a server or plurality ofservers. The risk map generation system 302 may update the downloadedrisk map using sensor information and environment information itobtained at steps 804 and 806. For example, the risk map generationsystem 302 may receive a risk map of a road segment that a vehicle 308is traveling along. The downloaded risk map may contain a risk valuebecause a pothole is located on the road segment. The risk mapgeneration system 302 may overlay an image on the downloaded risk mapaccording to the obtained sensor information and environmentinformation, e.g., adding an object indicating a wet road condition dueto weather information indicating that it is raining. The risk mapgeneration system 302 may calculate a new risk value of the road segmentusing an existing risk score and considering the other risk objects tocalculate an updated risk score. After step 808, the method may proceedto step 810.

At step 810, the risk map generation system 302 may generate a 3D riskmap using the sensor information, the environment information, and thedetermined risk values. In some instances, the risk map generationsystem 302 may generate a 3D risk map as previously described. In someembodiments, the risk map generation system 302 may generate a 3D riskmap using point clouds. In some aspects, the 3D risk map may begenerated using risk objects or road segments. In some instances, the 3Drisk map may comprise a point cloud real-time virtual environmentrepresenting the route or road segment the vehicle 308 is traveling onand the vehicle's 308 surroundings (environment). After step 810, themethod may continue to step 812.

At step 812, the risk map generation system 302 may display the riskmap. In some aspects, the risk map generation system 302 may transmitthe risk map to another computing device or mobile computing device 306for displaying the risk map. In some aspects, a computing device maydownload the risk map from the risk map generation system 302 in orderfor the risk map to be displayed. In some aspects, the risk mapgeneration system 302 may display a risk map to a user. In someexamples, the risk map may be displayed on the exterior of the vehicle308 (e.g., on the hood of a vehicle 308), on the interior of the vehicle308 (e.g., on a display device, LCD screen, LED screen, and the like),or on the windshield of the vehicle 308 (e.g., heads-up display (HUD)).In some embodiments, a risk map may be displayed as a hologram, onaugmented reality (AR) glasses, and the like. In some aspects, the riskmap generation system 302 may display the risk map as previouslydescribed. Upon completion of step 812, the method may proceed to step814.

At step 814, the risk map generation system 302 may store the risk map,the sensor information, the environmental information, and thedetermined risk values in a database. In some aspects, the informationand risk values may be stored together (e.g., tied to a particular riskmap or a particular road segment). In other aspects, the information maybe stored independently and categorized by road segment and differentcharacteristics of the information. For example, all data for a risk maprelated to a road segment traveled at night time may be stored together.In some embodiments, the risk map(s), sensor information, environmentinformation, road segment(s), and risk value(s) may be transmitted toanother device (e.g., insurance server) to be stored. In some aspects,the risk map(s), sensor information, environment information, roadsegment(s), and risk value(s) may be buffered. In some instances, therisk map(s), sensor information, environment information, roadsegment(s), and risk value(s) may only be stored for a certain amount oftime. For example, the risk map(s), sensor information, environmentinformation, road segment(s), and risk value(s) may only be stored for30 days at a time. In some aspects, a user or a system owner (e.g.,insurance provider) may determine how long the risk map(s), sensorinformation, environment information, road segment(s), and risk value(s)may be stored. In some instances, the risk map generation system 302 maystore the risk map(s), sensor information, environment information, roadsegment(s), and risk value(s) as previously described. After step 814,the method may proceed to step 816.

At step 816, the risk map generation system 302 may determine if thelocation of a vehicle 308 has changed. If the location of the vehicle308 has changed, the method may proceed to step 804. If the location ofthe vehicle 308 has not changed the method may stay at step 816. In someaspects, in order for the risk map generation system 302 to determinethat the vehicle's 308 location has changed the vehicle 308 may need tomove a certain distance past a threshold. The threshold may be a userselected threshold or a predetermined threshold. The threshold may beadjustable or fixed. In some embodiments, the risk map generation system302 may receive information from other devices to determine if thevehicle 308 has moved. For example, the risk map generation system 302may receive movement information from a vehicle telematics device. Asanother example, the risk map generation system 302 may receive movementinformation from a GPS device or mobile device. The GPS device mayprovide coordinates relating to the location of the vehicle 308 at afirst point in time, and may provide different coordinates relating tothe location of the vehicle 308 at a second point in time, thus, showingthe vehicle 308 has moved. Following step 816, the method may proceed tostep 818.

At step 818, the risk map generation system 302 may determine the costof insurance based on the risk map. In some aspects, the cost ofinsurance may be determined based on the risk values associated with therisk map. The risk map generation system 302 may determine an insurancepremium for a user based on the road segment traveled and the risk scoreassociated with the road segment. The cost of insurance may bedetermined after a trip has been completed by adding up the differentrisk values associated with the risk map. In some aspects, the risk mapgeneration system 302 may adjust the insurance premium for a user basedon the risk values associated with the risk map. Upon completion of step818, the method may proceed to step 820.

At step 820, the risk map generation system 302 may determine whether ornot an accident has occurred. If an accident has not occurred, themethod may proceed to step 804. If the risk map generation system 302determines that an accident has occurred, then the method may proceed tostep 822. The risk map generation system 302 may use accidentinformation and/or sensor information to determine that an accident hasoccurred. For example, information from a LIDAR sensor may be evaluatedto determine that the vehicle collided with another object. In anotherexample, information from a sensor installed in the vehicle (e.g.,original equipment manufacturer (OEM)), such as information indicatingthat an airbag was deployed, may be monitored to detect whether anaccident occurred.

At step 822, the risk map generation system 302 may analyze the risk mapinformation in order to determine the cause of the accident. The riskmap generation system 302 may analyze the stored risk maps that weredeveloped close to the time of the accident to determine the cause ofthe accident. The risk map generation system 302 may evaluate the riskobjects located within the risk map at the time of the accident as wellas the risk values associated with those risk objects. Following step822, the method may proceed to step 824.

In step 822, the risk map generation system 302 may determine, based onthe analyzing of the stored risk map, the cause of the accident. Forexample, if a risk object was located in front of a vehicle 308, andthere is damage to the front bumper of the vehicle 308, then the riskmap generation system 302 may determine there is a front-end accident.As another example, in response to determining that an accident hasoccurred, the risk map generation system 302 may attempt to determinewhat in the risk map is likely the cause of the accident. For example,knowing that there was an impact to the side of the vehicle, the riskmap generation system 302 may evaluate the risk map just before (or atthe same time or just after) the impact to identify which, if any,objects might have caused the impact. Then, in step 824, the risk mapgeneration system 302 may determine whether the cause of the accidentcould be determined. In some cases, the risk map generation system 302might not be able to determine the cause of the accident (e.g., noobject was identified or multiple objects were identified as possiblecauses). In some embodiments, the analysis at step 822 may includedetermining a confidence score indicating a confidence that the risk mapgeneration system 302 has in its determination of what may have led tothe accident. In such embodiments, step 824 may include comparing theconfidence score with a threshold to determine whether the risk mapgeneration system 302 is confident enough in its determination.Different thresholds may be used for different causes of an accident ordifferent drivers. If the cause of the accident is not determined (orthe risk map generation system 302 is not confident in its determinationof the cause), the method may proceed to step 828. If the cause theaccident is determined (or the risk map generation system 302 has enoughconfidence in its determination), then the method may proceed to step826.

At step 826, the risk map generation system 302 may determine whetherthe cause of the accident includes an accident listed on a predeterminedlist of accidents. The predetermined list may be formulated by aninsurance provider. An insurance provider may determine which accidentscan be automatically processed by the system, and add such accidents tothe predetermined list. For example, an insurance provider may determinethat collisions with objects rather than other vehicles, may beprocessed automatically, and thus, may be added to the list. Incontrast, if the cause of the accident was contact with another vehicle,it might not be readily apparent which driver is at fault (and thus,which insurance company is responsible), and thus, such types ofaccidents might not be on the list. In some examples, the list maycomprise an animal, poor road conditions (e.g., a pothole), a flat tire,and a chipped windshield. If the cause of the accident is not one on thepredetermined list, the method may proceed to step 828. If the cause ofthe accident can be categorized as one of the types of accidents on thepredetermined list, the method may proceed to step 840.

At step 828, the risk map generation system 302 may forward the accidentinformation to an agent of an insurance company for processing. Forexample, the risk generation system 302 may transmit the risk map andall associated information (e.g., risk values, road segments, sensorinformation, etc.) to a server (e.g., a server operated by an insuranceprovider). In some embodiments, the risk map may be transmitted to auser device (e.g., mobile computing device 306) or a computing deviceassociated with an insurance agent. Upon receiving the risk map, theinsurance agent may be able to evaluate the damage to the vehicle 308and determine the cause of the accident. In some examples, the risk mapmay be provided to a user device associated with the police.

At step 840, the risk map generation system 302 may construct aninsurance claim. Here, the constructed insurance claim may be aparticular data structure including various information used forprocessing insurance claims. The data structure may include, inassociation with each other, a driver's name, a policy number of anapplicable insurance policy for the vehicle, a premium of the applicableinsurance policy, a deductible of the insurance policy, an amount of theclaim, an indication of an amount of damage to the vehicle as a resultof the accident, an image of the damage, a description of the damage,the determined cause of the accident, a location of the accident, adate/time of the accident, a date/time of constructing the insuranceclaim, etc. The risk map generation system 302 may use accidentinformation, sensor information, cause of the accident information,environmental information, and vehicle information to construct aninsurance claim. The risk map generation system 302 may also use riskmap information and risk values to help construct the insurance claim.Once the insurance claim has been made, the method may proceed to step842.

At step 842, the risk map generation system 302 may store the insuranceclaim. In some aspects, the risk map generation system 302 may store theinsurance claim information in a claims database. In some aspects, thecomputing device may transmit the insurance claim information to aninsurance provider server or another computing device that may store theinsurance claim information. In some embodiments, the insurance claimmay be stored with all associated data (e.g., risk map(s), riskvalue(s), etc.) to the accident. In some examples, the risk mapgeneration system 302 may store the insurance claim on an insuranceprovider's server or on a user device (e.g., mobile computing device306). Upon completing step 842, the method may proceed to step 844.

At step 844, the risk map generation system 302 may notify a user bytransmitting, to a user device, the insurance claim information. In someaspects, the risk map generation system 302 may transmit an alert to auser device to notify a user that an insurance claim has been made forthe user. Such a service may be appreciated by customers. That is,customers may appreciate that a system has automatically placed aninsurance claim so that they do not have to and can focus on othermatters. Some customers may also be appreciative when the systemautomatically places a claim for them, because they may take comfortknowing that their insurance claim is being handled in a timely manner.In accordance with aspects of the present disclosure, notification of aninsurance claim may take place within just hours, minutes, or seconds ofthe accident occurring. In some examples, the risk map generation system302 may push a notification to the user. Additionally, or alternatively,a user may be able to use a mobile computing device 306 or another usercomputing device (e.g., desktop computer) to download insurance claiminformation from the risk map generation system 302. In light of theabove, it should be understood that an insurance company/provider, usinginformation from the risk map, may provide insurance claim statusinformation on a website just minutes after a customer's vehicle isinvolved in an accident.

At step 846, the risk map generation system 302 may evaluate the claimby determining the cost of the claim. Evaluating the claim may requirethe risk map generation system 302 to determine the cost of theaccident. The cost of the accident may be determined by evaluating thecost to fix the vehicle(s) 308, and cost of injury to any passenger(s)in the vehicle(s) 308. In some aspects, as part of the evaluation, therisk map generation system 302 may determine whether any property wasdamaged and a cost to fix the damaged property. In some embodiments, therisk map generation system 302 may analyze a user's insurance policy todetermine a user's coverages, and determine the amount of the claim thatmay be covered by insurance and the amount of the claim that may have tobe paid by the user. After step 846, the method may continue to step848.

At step 848, the risk map generation system 302 may determine whetherthe claim is eligible to be processed automatically or whether the claimshould be forwarded to an insurance agent/adjuster for processing. Thedetermination in step 848 may include determining whether the cost ofthe insurance claim is below or above a threshold. In some aspects, thethreshold of the cost of the insurance claim may be determined andprovided by an insurance company/provider. In some instances, thethreshold may be set by a user (e.g., driver involved in the accident,policy holder, etc.). For example, if the cost of a claim was $500, andthe user's threshold was set to $1,000, then the claim is below thethreshold and the claim may be eligible for automatic processing. If thevalue or cost the insurance claim is above the threshold value, the riskmap generation system 302 may determine that the claim is not eligiblefor automatic processing at which point the method may proceed to step828. At step 828, the risk map generation system 302 may send theprocessing claim to an agent/adjuster of an insurance provider in orderto complete the claim processing. The insurance agent/adjuster maydecide that he/she needs to inspect the vehicle before settling theclaim. For example, if the insurance agent/adjuster suspects foul play(e.g., insurance fraud), the insurance adjuster may wish to investigatethe insurance claim. In an alternative embodiment, a user may select athreshold price for processing a claim. For example, if a user selects aclaim processing threshold of $1000 and the value of the claim is $500,then the claim may not be processed (e.g., the user would be willing topay for cost of the accident out of pocket). If the cost of theinsurance claim is below the threshold value, the method may proceed tostep 850.

At step 850, the risk map generation system 302 may automaticallyprocess the insurance claim. Such automatic processing may allowinsurance companies to use their resources (e.g., employees such asinsurance adjusters) efficiently. In some instances, step 850 mayinclude reimbursing the user involved in the accident by electronicallydepositing money into a user's money account. In some aspects, the riskmap generation system 302 may initiate a process for having a refundcheck sent to the user. In some aspects, the risk map generation system302 may transmit reimbursement information to a server or computingdevice of the insurance provider of the user in order for the insuranceprovider to provide the user with a reimbursement. In some aspects, therisk map generation system 302 may provide claim information to aninsurance provider or vehicle repair facility so a mechanic or placewhere the vehicle 308 may be repaired may be compensated. In someembodiments, processing the claim may include the risk map generationsystem 302 identifying and notifying a tow truck service and car repairfacility for repairing the vehicle 308. In some examples, processing theclaim may cause the risk map generation system 302 to send and receiveinformation related to the insurance claim to various computing devicesassociated with repair facilities to acquire vehicle repair costinformation. In some embodiments, processing the claim may include therisk map generation system 302 selecting a vehicle repair facility basedon a user's preference or insurance provider's preference. Uponcompletion of step 850, the method may proceed to step 852.

At step 852, the risk map generation system 302 may determine whether ornot to adjust the user's insurance. In some aspects, the risk mapgeneration system 302 may adjust the user's insurance premium based onthe accident. In some examples, the risk map generation system 302 mayadjust the premium by raising or lowering the price. In other aspects,the risk map generation system 302 may adjust the coverages of theuser's insurance policy. For example, the user's liability coverage andcollision coverage may be increased or decreased. In some aspects, therisk map generation system 302 may determine whether or not to adjust auser's insurance based on the analyzing that occurred at step 822 of themethod. In some instances, the risk map generation system 302 may adjusta user's insurance based on the claim processing of step 846. If therisk map generation system 302 determines not to adjust the user'sinsurance, the method may proceed to step 830, and the process may end.If the risk map generation system 302 determines to adjust the user'sinsurance, then the method may proceed to step 854. At step 854, theadjustment may be calculated and put into effect, and the user may benotified of the changes to their insurance policy. In some examples, therisk map generation system 302 may generate and transmit an email to bereceived by a user device. In other examples, the risk map generationsystem 302 may alert another server or computing device responsible forcontacting the user and alerting them of the changes to their policy.

As with the methods of the aforementioned FIGS. 6, 7, 8A, 8B, and 8C,steps may be added, omitted, or modified to the methods of the FIGS. 6,7, 8A, 8B, and 8C.

The foregoing descriptions of the disclosure have been presented forpurposes of illustration and description. They are not exhaustive and donot limit the disclosure to the precise form disclosed. Modificationsand variations are possible in light of the above teachings or may beacquired from practicing of the disclosure. For example, where thedescribed implementation includes software, it should be understood thata combination of hardware and software or hardware alone may be used invarious other embodiments. Additionally, although aspects of the presentdisclosure are described as being stored in memory, one skilled in theart will appreciate that these aspects can also be stored on other typesof computer-readable media, such as secondary storage devices, like harddisks, floppy disks, or CD-ROM; a carrier wave from the Internet orother propagation medium; or other forms of RAM or ROM.

What is claimed is:
 1. A system comprising: a server storing templatemap data; a global positioning system device configured to determinegeographic location information for a vehicle; one or more sensorscoupled to the vehicle and configured to detect sensor information; anda first computing device configured to: communicate with the server toreceive the template map data comprising environmental informationindicating environments along road segments for generating one or morethree-dimensional (3D) virtual worlds representing the environments;communicate with the global positioning system device to receive thegeographic location information; determine, based on the geographiclocation information, a particular road segment on which the vehicle islocated among the road segments of the template map data; determine,based on a portion of the environmental information of the template mapdata corresponding to the particular road segment, a particularenvironment along the particular road segment; generate a particular 3Dvirtual world representing the particular environment along theparticular road segment on which the vehicle is located; communicatewith the one or more sensors to receive the sensor information; analyzethe sensor information to identify one or more risk objects; generateone or more point clouds illustrating the one or more risk objects,respectively; generate, by superimposing the one or more point cloudsinto the particular 3D virtual world, a 3D risk map; display, using adisplay device of the vehicle, the 3D risk map; determine a risk scorefor at least one of the one or more risk objects; output a first alertif the risk score is above a predetermined threshold; and output asecond alert if the risk score is below the predetermined threshold. 2.The system of claim 1 wherein the first computing device is configuredto: determine a plurality of at risk objects based on the sensorinformation; determine a risk score for each of any additional ones ofthe plurality of at risk objects; and filter the plurality of at riskobjects based on the risk score for each object to identify the one ormore risk objects.
 3. The system of claim 1, wherein the first computingdevice is further configured to: determine a route of travel of thevehicle; calculate a route risk score of the route; and update, based onthe route risk score, the 3D risk map to indicate a level of riskassociated with the route.
 4. The system of claim 1, wherein the firstcomputing device is further configured to: determine, based on the 3Drisk map, one or more road segments the vehicle has traveled; calculateone or more road segment risk scores for the one or more road segments;and transmit the one or more road segment risk scores.
 5. The system ofclaim 1, wherein the first computing device is further configured to:determine a risk score for at least one of the one or more risk objects;output a first alert if the risk score is above a predeterminedthreshold; and output a second alert if the risk score is below thepredetermined threshold.
 6. The system of claim 1, wherein the firstcomputing device is further configured to: output an audio alert basedon the risk score exceeding a threshold.
 7. The system of claim 1,wherein the first computing device is further configured to: assign acolor to each of the one or more risk objects, wherein the color isbased on the risk score and represents a level of risk for each of theone or more risk objects.
 8. The system of claim 7, wherein the firstcomputing device is further configured to: update the 3D risk map tochange the color of a particular risk object among the one or more riskobjects to red if the level of risk associated with the particular riskobject is greater than a predetermined threshold.
 9. The system of claim1, further comprising: a second server configured to store weatherinformation, wherein the first computing device is further configured tocommunicate with the second server to receive the weather information,and wherein generation of the particular 3D virtual world is furtherbased on the weather information, such that the particular 3D virtualworld illustrates a weather condition.
 10. The system of claim 1,further comprising: one or more second sensors coupled to a secondvehicle or an element of infrastructure, wherein the one or more secondsensors are configured to detect second sensor information; and a secondcomputing device configured to: analyze the second sensor information toidentify one or more second risk objects; and transmit additionalinformation indicating the one or more second risk objects, wherein thefirst computing device is further configured to: receive the additionalinformation transmitted by the second computing device; based on theadditional information, update the 3D risk map by superimposing one ormore second point clouds corresponding to the one or more second riskobjects, respectively, into the particular 3D virtual world; and displaythe updated 3D risk map to a person in the vehicle.
 11. The system ofclaim 1, wherein the first computing device is further configured todynamically update the 3D risk map to provide an updated virtual worldrepresenting environments along subsequent road segments as the vehiclemoves.
 12. The system of claim 1, wherein the first computing device isfurther configured to: determine that the vehicle was in an accident;analyze the 3D risk map to determine a cause of the accident; determineif the accident is on a pre-determined list of accidents that indicatewhich accidents are eligible for insurance claim processing by thesystem; and if the accident is on the pre-determined list, construct,based on the cause of the accident, an insurance claim.
 13. The systemof claim 12, wherein the first computing device is further configuredto: compare information regarding damage to the vehicle with a locationof the one or more risk objects within the 3D risk map.
 14. The systemof claim 12, wherein the first computing device is further configuredto: transmit a notification to a driver or owner of the vehicle inresponse to constructing the insurance claim.
 15. The system of claim12, wherein the first computing device is further configured to: adjustan insurance premium of an insurance policy associated with the vehiclebased on the cause of the accident.
 16. A method comprising: receiving,by a computing device and from a server, template map data comprisingenvironmental information indicating environments along road segmentsfor generating one or more three-dimensional (3D) virtual worldsrepresenting the environments; receiving, by the computing device andfrom a global positioning system device, geographic location informationfor a vehicle; determining, based on the geographic locationinformation, a particular road segment on which the vehicle is locatedamong the road segments of the template map data; determining, based ona portion of the environmental information of the template map datacorresponding to the particular road segment, a particular environmentalong the particular road segment; generating a particular 3D virtualworld representing the particular environment along the particular roadsegment on which the vehicle is located; receiving, by the computingdevice from one or more sensors coupled to the vehicle, sensorinformation; analyzing, by the computing device, the sensor informationto identify one or more risk objects; generating, by the computingdevice, one or more point clouds illustrating the one or more riskobjects, respectively; generating, by the computing device, a 3D riskmap by superimposing the one or more point clouds into the particular 3Dvirtual world; outputting, by the computing device and to a displaydevice of the vehicle, the 3D risk map for display; determining a riskscore for at least one of the one or more risk objects; outputting afirst alert if the risk score is above a predetermined threshold; andoutputting a second alert if the risk score is below the predeterminedthreshold.
 17. The method of claim 16, further comprising: determining aroute of travel of the vehicle; and calculating a route risk score ofthe route, wherein the generating the 3D risk map is further based onthe route risk score.
 18. The method of claim 16, further comprising:determining a road segment associated with a location of the vehicle;calculating a road segment risk score for the road segment; andupdating, based on the road segment risk score, the 3D risk map to alerta person about a level of risk associated with the road segment.
 19. Themethod of claim 16, further comprising: outputting an alert after adetermination that the risk score is above a predetermined threshold.20. An apparatus comprising: one or more processors; and memory storingcomputer-executable instructions that, when executed by the one or moreprocessors, cause the apparatus to: receive, from a server, template mapdata comprising environmental information indicating environments alongroad segments for generating one or more three-dimensional (3D) virtualworlds representing the environments; receive, from a global positioningsystem device, geographic location information for a vehicle; determine,based on the geographic location information, a particular road segmenton which the vehicle is located among the road segments of the templatemap data; determine, based on a portion of the environmental informationof the template map data corresponding to the particular road segment, aparticular environment along the particular road segment; generate aparticular 3D virtual world representing the particular environmentalong the particular road segment on which the vehicle is located;receive, from one or more sensors coupled to the vehicle, sensorinformation; analyze the sensor information to identify one or more riskobjects; generate one or more point clouds illustrating the one or morerisk objects, respectively; generate a 3D risk map by superimposing theone or more point clouds into the particular 3D virtual world; output, adisplay device of the vehicle, the 3D risk map for display; determine arisk score for at least one of the one or more risk objects; output afirst alert if the risk score is above a predetermined threshold; andoutput a second alert if the risk score is below the predeterminedthreshold.